Image-to-image (I2I) translation refers to the task of learning a mapping function between the source domain and target domain, which preserves the content of the source image while merging the style of the target domain at the same time. According to many I2I methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, I2I methods have developed towards multi-mapping [4]}, [6]}, [5]} and few-shot learning [7]}. CycleGAN [2]} introduces the cycle-consistency into generative models, which enables I2I methods to train cross-domain translation without paired data. FUNIT [7]} accomplish the style transfer task by encoding content and style respectively and combine them with adaptive instance normalization (AdaIN) [14]}. From an intuitive thought, font generation is a typical I2I translation task, since it tries to keep the content information of the source font and maps it into the target font. Thus, many font generation methods are based on I2I translation methods.
